Ask HN: Burnout because of ChatGPT?

I do Mac/iOS development and am constantly asking ChatGPT about various APIs and frameworks. Apple's documentation is not great for explaining how to actually use APIs, unless you can find the one WWDC video that explains it or a sample project that they released years ago. I would normally google for sample code, blog posts, tutorials, or Stack Overflow posts. Something that might take an hour of searching and reading now takes a few seconds of just asking ChatGPT.

Even for things that I've done before, it's often much easier to ask ChatGPT how to do something than to look through my projects to find how I did it previously. It might sound lazy, but if it takes me several minutes to search through various projects to find that one time I did something, why bother when I can just ask ChatGPT and know in seconds?

I will say that yes, ChatGPT can hallucinate APIs that don't exist, and that can be annoying, but even if it does it 20% of the time, it's still incredibly valuable in the time savings the other 80% of the time it does hit.

I think it's pretty useful to share concrete examples like this. I basically always have a ChatGPT4 CI tab open these days when I'm developing these days - it's a usually much faster/better go-to than Google or SO for looking stuff up. ChatGPT is great for all the random stuff I don't care to remember the syntax for.

* I'm always using it to munge/generated tables/csv/markdown/json - you can basically throw any copy and paste from a random PDF that's some weird gobbedlygook of tabs, spaces, newlines and get something cleanly formatted. On the one hand, it seems like a waste of computation, but on the other hand, it's way cheaper than my time and there are so many tasks that require using poorly formatted output. Even better, CI will of course write awk/sed for you if you need to do any automation.

* I'm always forgetting the syntax for named byobu sessions (it happily wrote a script to help with that) but I've also been staging some dev servers and it was able to generate the scripts to create new named session and windows, attaching/creating when necessary, handling if the processes were running, and creating the systemd units for spinning these up.

* On this same project it wrote some python scripts for managing SSH tunnels and reverse tunnels, including filtering/logging of error messages, handling jump servers, etc. This is all stuff I've done years ago (and even written lots of docs for), but it was actually way faster for ChatGPT to generate these than digging those out.

* I've been running into issues w/ some HTML5 audio output and needed to swap to websocket streaming w/ webmedia output (which I wasn't familiar with at all). ChatGPT gave me the code to swap into my FastAPI server and the frontend code I had w/o having to do any further research, great.

* I hate Docker setups, and I had issues w/ Nvidia containers and GPUs not showing up w/ my docker config. I was able pass the various error messages and get my problems fixed without spelunking/hair-pulling. Same with figuring out some cross-container network hijinx.

* There's a bunch of one-offs that I might just not have bothered doing, that I can just ask it to do as well - eg, I've previously written code for poisson distributions and the like, so I knew what to ask for, but would have been a huge PITA to dig out exactly how to do it, but took like no effort to just ask GPT4 CI to figure out a one off I just wouldn't have done otherwise: https://chat.openai.com/share/80fa7bc0-e099-4577-bad9-d026e7...

"new developments and new tools and frameworks and models keeps me feeling like I am stuck in quicksand with lead boots while all these developments are whizzing past me"

Exactly, even as somebody with a background in NLP, some things that I developed over the course of two-three months in Summer 2022 were promptly replicated and nicely put into accessible, open source modules by winter that anybody could run in a few lines of code, besides some bespoke bits like processing (QA on documents, etc). After a few such experiences, you begin to become wary of working on the edge, since the progress is so fast you could waste lots of time on convergent evolution. It really is hard to predict any part that won't succumb to that in the current racing stage.

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GPT explains things in a personalised way, it's in no way in the same league as manuals. It's each user's native language.